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Accuracy vs. Complexity: A Trade-off in Visual Question Answering Models
However, modelling the visual and semantic features in a high dimensional (joint embedding) space is computationally expensive, and more complex models often result in trivial improvements in the VQA accuracy.
Question-Agnostic Attention for Visual Question Answering
Visual Question Answering (VQA) models employ attention mechanisms to discover image locations that are most relevant for answering a specific question.
From Known to the Unknown: Transferring Knowledge to Answer Questions about Novel Visual and Semantic Concepts
To evaluate our model, we propose a new split for VQA, separating Unknown visual and semantic concepts from the training set.
Reciprocal Attention Fusion for Visual Question Answering
Existing attention mechanisms either attend to local image grid or object level features for Visual Question Answering (VQA).
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